Spatialization of soil quality index in the Sub-Basin of Posses , Extrema , Minas Gerais

Espacialização do índice de qualidade do solo na Sub-Bacia das Posses, Extrema, Minas Gerais R E S U M O Objetivou-se, com este estudo, determinar o índice de qualidade do solo (IQS), em relação a atributos químicos e físicos, e espacializá-lo, além de avaliar a utilização deste índice no pagamento por serviços ambientais na Sub-Bacia Hidrográfica das Posses, Extrema, Minas Gerais, representativa do Bioma Mata Atlântica. Os valores do IQS foram influenciados tanto pela substituição da mata nativa por povoamento de eucalipto, quanto por pastagens e culturas anuais, refletindo na redução da qualidade do solo na profundidade amostrada nos sistemas avaliados. A espacialização do IQS apresentou valores variando de 0,40 a 0,80, ocorrendo algumas áreas pontuais com elevados índices e algumas com índices superiores a 1,00 (mata nativa). O reflorestamento com eucalipto condicionou solos, em sua maioria, com baixas deteriorações físicas e químicas devido ao acúmulo de serrapilheira. Já os menores valores do IQS estão associados às pastagens. O modelo pelo qual o IQS se ajustou foi o exponencial, possibilitando a krigagem ordinária. O IQS apresenta grande potencial para uso no pagamento de agricultores que prestam serviços de conservação do solo e água.


Introduction
According to Doran & Parkin (1994), soil quality is defined as the capacity of the soil to function within the limits of the ecosystem, managed or natural, in order to sustain biological production and maintain environmental quality and the health of plants and animals.Therefore, it is the capacity of the soil to perform its functions in nature, acting as a medium for plant development; regulation and compartmentalization of water flow in the environment; stock and promotion of cycling of elements in the biosphere and as an environmental buffer in the formation, attenuation and degradation of compounds that are harmful to the environment (Vezzani & Mielniczuk, 2009).
However, soil quality cannot be directly evaluated; it must be inferred from soil quality indicators that are used by farmers and scientists (Mairura et al., 2007).Thus, soil physical, chemical and biological attributes can be used as quality indicators, allowing the measurement of the capacity of the soil to perform its essential functions in favor of a sustainable management.
In this perspective, it is essential to select a minimum set of indicators that have characteristics such as easy evaluation, applicability on different scales, capacity of integration, adequacy to the research analysis level, utilization in the highest possible number of situations, sensitivity to variations in management and climate, and possibility of measurements through quantitative and/or qualitative methods.
Studies developed by Swanepoel et al. (2014) verified the importance of the evaluation of the soil quality index (SQI), in relation to the sustainability of agricultural systems, and confirmed low SQI values inadequately managed soils.
In the evaluation of different systems of planted forest and native cerrado, in different regions of the state of Minas Gerais, Freitas et al. (2012) obtained SQI values that prove that these forest management areas suffer a reduction in the indices compared with the evaluated native systems.
Some research lines have proposed, as soil quality indicators, the evaluation of soil physical, chemical and biological attributes (Lima, 2013;Nesbitt & Adl, 2014).Organic matter has also been included, for its importance in nutrient availability, soil structure and erosion control, water retention and the transport and immobilization of pollutants (Barrios et al., 2006;Fließbach et al., 2007).
According to Hazarika et al. (2014), soil quality can also be evaluated through the soil deterioration index, for which the deviations of soil chemical and physical properties of an area under anthropic action are compared with the baseline of an adjacent natural area or an area with similar conditions of soil and climate.
By establishing the indices in georeferenced points, it is possible to spatialize them using geostatistical tools, which provide better understanding on their variation and behavior in the environment.Studies on spatial variability of soil attributes are important not only in samplings or data interpretation, but also in soil survey and classification (Lima et al., 2010;Grego et al., 2011).
Therefore, the evaluation of soil quality in fragile biomes, such as the Atlantic Forest, is of great importance, because it concentrates a large portion of the Brazilian population (70%) and with percentage of conserved forest remnants of only 7.26% (Brasil, 2007).Thus, SQI determination in areas with forest remnants, especially in the areas of water recharge of the Cantareira System, which supplies the state of São Paulo, constitutes a relevant tool in the implementation of adequate management practices that promote environmental sustainability.
In this context, this study aimed to determine and spatialize SQI and evaluate its use in the payment for environmental services in areas under agricultural use, forests, pastures and remnants of the Atlantic Forest in the Sub-Basin of Posses, Extrema-MG, Brazil.

Material and Methods
The studied area is located in the municipality of Extrema-MG, Brazil, in the Sub-Basin of Posses, at the UTM coordinates 374500 and 371500 E and 7468200 and 7474800 S (Datum SAD 69, Zona 23S) (Figure 1).The climate in the region is mesothermal, with mild summers and dry winters, Cwb, according to Köppen's climate classification.
The predominant vegetation is in the biome of the Atlantic Forest (ANA, 2008).The sub-basin is located in the Jaguari River Basin, one of the rivers that supply the reservoir of the Cantareira System in the state of São Paulo.
Currently, the main soil use is pasture and most areas have been poorly managed; in addition, there are stands of eucalyptus, annual crops and native forest (Silva et al., 2013) (Figure 3 and Table 1).
Prior to sample collection, a stratified soil sampling grid was generated, with 150 points, spaced by 350 m and distributed in an area of approximately 1,200 ha (Figure 4), using the program ArcGIS 9.3.In areas with higher variability on the landscape, a larger number of sampling points was used, in order to increase their representativeness.
The samples were collected in order to represent the five soil classes prevailing in the sub-basin and the main   Representative soil profiles were dug, considering the class and current use of the soil for the collection of disturbed and undisturbed samples.The evaluation of soil quality was performed through the SQI, using the model suggested by Islam & Weil (2000).According to Araújo et al. (2007), for the application of the model, a few basic assumptions must be made: the natural ecosystems, characterized by minimum anthropic intervention and expected equilibrium, are considered as a reference; two categories of soil quality attributes (chemical and physical) contribute equitably to soil quality and the same weighted value For SQI determination, the attributes involved in the main functions performed by the soil (Table 2) were considered.The analysis of soil chemical and physical attributes were performed by Lima et al. (2014). where: Q a -mean of the deviations of the indicators of each attribute in relation to the reference; w -value of the indicator measured in the studied systems; k -value of the indicator measured in the reference system; n -number of indicators constituting each set of attributes; Qca -mean of the deviations of soil chemical attributes; and Qpa -mean of the deviations of soil physical attributes.
After one SQI was generated for each sampled point, the deterioration indices of soil chemical and physical attributes and the SQI corresponding to each soil class and main uses were determined.The deterioration is considered as the chemical and physical variations of the managed areas in comparison to the native ones.The R program (R Core Team, 2014) was used for the descriptive analysis for each soil class, which provided: mean, standard deviation, coefficient of variation and asymmetry.
The adjustment parameters of the experimental semivariogram for SQI, as well as the geostatistical analysis, were obtained using the R program (R Core Team, 2014), in the GeoR package (Ribeiro Júnior & Diggle, 2001), performed through the analysis of semivariograms based on the assumptions of intrinsic hypothesis, in which the spatial dependence ratio is the same at any "h" position inside a certain range of the spatial continuity.
For each soil attribute, the semivariances g(h) were calculated in all directions, meeting the hypothesis of isotropy.After adjusting the mathematical model, the following parameters were defined: nugget effect (C0), g value when h is zero; range (a), value of h when g stabilizes close to a constant value; (C1), structural variance and sill (C1 + C0), value of g when a constant value is obtained close to the variance of the data.The spatial dependence ratio (SDR) between samples was determined according to Cambardella et al. (1994).
After obtaining the data necessary for Kriging, the maps were constructed using the R program (R Core Team, 2014).

Results and Discussion
The deterioration suffered by soil chemical and physical attributes for each soil use and each soil class, and the respective soil quality indices, are shown in Table 3.
The SQI values calculated from the deviations of soil properties in the systems of reforestation, annual crops and pastures, compared with the reference natural system (native forest), were influenced by both the replacement of native forest by the stand of eucalyptus and pasture and annual crops, reflecting in the reduction of soil quality in the sampled layer in the evaluated systems.Cardoso et al. (2011) observed, in the superficial soil layer, the highest contents of organic matter, which mainly came from the deposition of organic substrate in the litter, where the effect of animal trampling was more pronounced and the activity of soil microbiota on the decomposition and mineralization of (2) the organic matter was more intense.Therefore, in this layer, soil chemical and physical attributes were more sensitive to the alterations imposed by anthropic action.
The alterations in the chemical attributes consisted of deteriorations in relation to the reference (NF) for all soil classes and uses, except for the chemical attributes in the uses of annual crops in area of CX and reforestation in area of RY.
The preparation of the soil for potato cultivation involves fertilization with nutrients that promote better soil fertility; in this case, soil quality improved 0.1% in relation to NF.However, for soil physical attributes, potato cultivation in the CX promoted a deterioration of 1.7% in relation to NF, certainly due to the management during harvest, which may have affected soil structure.
Potato cultivation is frequently performed in soils with moderate to high declivity, as the situation in the Sub-Basin of Posses, and the soil is intensively prepared and susceptible to losses by water erosion, compaction, reduction in water infiltration rates and a consequent decrease in water table recharge, which damage the environment and even make it difficult to obtain high yields.
The area with eucalyptus reforestation in RY showed improvement of 3.39% in soil chemical attributes, in relation to NF; this is due to the soil correction performed before eucalyptus was planted and the significant CEC variations in soils under eucalyptus cultivation (Effgen et al., 2012).Studies have shown the accumulation of N, P, K, Ca and Mg in leaves, branches and barks, contributing to the increase of litter, nutrient cycling and organic residues (Stape et al., 2010).
As to the physical attributes, a deterioration of approximately 15% occurred for the eucalyptus reforestation in RY.The eucalyptus plantations in the Sub-Basin of Posses generally occur in areas of degraded pastures and this previous soil use can influence the deterioration of soil physical attributes, especially in RY areas.
The soil use with pasture, which represents more than 70% of the Sub-Basin of Posses, showed the lowest SQI compared with NF and the other uses, in all the evaluated soil classes, showing the highest deteriorations in both chemical and physical attributes.The lowest SQI value (0.276) corresponds to the use under pasture in PVA.In this situation, areas with degraded pasture were found, i.e., areas with occurrence of laminar erosion, usually in areas with undulating to strongly undulating landscape.
One of the main causes of pasture degradation is the reduction of soil fertility due to the loss of nutrients through the production process (animal feeding), erosion, leaching and volatilization (Fonte et al., 2014).In addition, one of the main effects caused by the animals on pastures is compaction, which increases soil density and decreases macroporosity, hampering soil water movement and root growth (Swanepoel et al., 2014).
Therefore, SQI can become an instrument to be used by the authorities in the payment for environmental services.Areas that maintain better SQI have lower degradation degree of soil chemical and physical attributes, and farmers must be valued.Thus, the index has the potential to reflect the state of conservation or deterioration of a small farm, allowing the use of rewards or penalties according to its value.
Based on the adjustment parameters of the semivariogram (Table 4), the obtained value of SDR indicates a moderate spatial dependence of SQI, according to Cambardella et al. (1994).Thus, it was possible to interpolate values in any position of the studied area, constructing maps through ordinary Kriging and using structural properties of the semivariogram of the sampled sites (Figure 5).2; The reforestation with eucalyptus conditioned most of the soils with low physical and chemical deterioration, probably due to the accumulation of litter.
3. The soil quality index adjusted to the exponential model, allowing the use of ordinary Kriging.

Figure 1 .
Figure 1.Sub-Basin of Posses in the municipality of Extrema-MG, Brazil.Adapted from Silva (2013)

Figure 2 .
Figure 2. Map of soil classes (A) and relief phases (B) of the Sub-Basin of Posses, in municipality of Extrema-MG, Brazil.Adapted fromSilva (2013)

Figure 4 .
Figure 4. Sampling points in the Sub-Basin of Posses, Extrema-MG, Brazil is attributed to each category; their respective indicators have the same relative importance.For SQI determination, the attributes involved in the main functions performed by the soil (Table2) were considered.The analysis of soil chemical and physical attributes were performed byLima et al. (2014).

Table 3 .
Figure 5. Semivariogram of the soil quality index (SQI) in the Sub-Basin of Posses, Extrema-MG, Brazil

Table 2 .
Soil functions and quality indicator attributes SQI was calculated in two steps: